readers of their paper may be surprised to hear that there is any uncertainty about the trend decline since the mid-1990s; their main graph has 30 annual data points since 1995. But these are not real data points in any obvious sense; rather they are synthetic (model-based) extrapolations based on national accounts and growth forecasts.

We have national household surveys for all but 10 of the 48 countries in SSA since 1995. However, for only 18 countries do we have more than one survey since 1995; for 30 countries, there are is at most one survey since 1995.

Pinkovskiy and Sala-i-Martin are doing the best that can be done with bad data: they use the scant surveys to get the shape of the income distribution, but discard what the surveys tell us about income levels. They calculate levels and poverty rates by tying the distribution to national income data.

Another caution: Africa is halving poverty using only the more optimistic data, the Penn World Tables. Chatting with Michael Clemens, he points out that World Bank data show poverty falling, but by far less. Arvind Subramanian critiques the Penn series here.

I think there are two short stories here. First, poverty has fallen a lot in Africa, and that’s good news. Maybe it’ll halve by 2015, maybe later. But there is happy news from the South. No one disagrees there.

Second, never, ever take data from low income countries too seriously. Doesn’t it strike you as odd that the World Development Indicators have annual infant mortality data for most countries in Africa for most years? It should. Most of that data is interpolated, and the rest is (as often as not) close to made up. It’s not just the human development indicators. You wouldn’t want to be inside the sausage factory that is the GDP calculation in Chad.

See Pogge and Reddy on global poverty measurement, which they (correctly) argue is unknown. http://www.socialanalysis.org. Both Sala-i-Martin and Ravallion are using a flawed and conceptually unclear idea of what poverty is, then using data that is not comparable across contexts of over time, at a poverty line that is unjustified. Absent information on the costs of the smaller basket of goods consumed by the poor, both estimates are meaningless. Even if that information were available, we still would only be calculating poverty in the narrow sense of sufficient income to cover a small cost of goods, excluding multidimensional deprivations that almost everyone now recognize as a necessary part of any plausible analysis of poverty.

The PWT are notably inconsistent between vintages. Pretty much any model selection methodology in a growth context (ala Sala-i-Martin, Doppelhoffer etc.) yields different conclusions dependent on which dataset you use. Could partly explain why the growth literature is so inconsistent. I’ve a paper under review which shows just this, but guess it’ll get rejected on the grounds that i’m not Simon Johnson ;o)

The diversity and differences in Africa, especially in relation to incomes can be so wide as to make statistical generalities meaningless. I guess the answers to some of the questions that beg for more quantitative information than we can get lies on development/increased use of more vigorous qualitative methods.

The problem with poverty analyses in Africa is that while the vast array of poverty students are providing answers we have have yet to pose the right questions and use Africa-specific definitions and methodologies. An example is if you were to look at Kenya the life expectancy at birth is 57.86 Years. Now look at infant mortality rate which is 54.7 deaths/1,000 live births. Any person who crosses age five suddenly has a much higher chance of living to over 70 years. Before one reached 35 years HIV and AIDS takes it toll. The life expectancy of one who lives to over 35 is then presumably much higher. Various regions in Kenya also have extreme differences. In Siaya District alone in Kenya the infant mortality rates range 80 to 200 per 1,000 births. And all these data have to be taken with a pinch of salt. With all these age, gender, geographical, ethnic and other variations; and with inability to collect sensible data within this diversity, national statistics seem rather hollow, save for the fact that is is used by most national, international authorities to define priorities. I think that the time to pose questions about descriptions and methodological issues is now. So far we have gotten rights statistical answers to the wrong questions…

I’m an Associate Professor of Political Science & International and Public Affairs at Columbia University. I use field work and statistics to study poverty, political engagement, the causes and consequences of violence, and policy in developing countries. [Read more]